Digital images are formed by many devices and used for many practical purposes. Devices include cameras with image sensors operating on visible or infrared light, such as a charge-coupled device (CCD) image sensor or a complementary metal-oxide-semiconductor (CMOS) image sensor, line-scan sensors, flying spot scanners, electron microscopes, X-ray devices including computed tomography (CT) scanners, magnetic resonance imagers, and other devices known to those skilled in the art. Practical applications are found in industrial automation, medical diagnosis, satellite imaging for a variety of military, civilian, and scientific purposes, photographic processing, surveillance and traffic monitoring, document processing, and many others.
To serve these applications, the images formed by the various devices are analyzed by machine vision systems to extract appropriate information. One form of analysis that is of considerable practical importance is determining the position, orientation, and size of patterns in an image that correspond to objects in the field of view of the imaging device. Pattern detection methods are of particular importance in industrial automation, where they are used to guide robots and other automation equipment in semiconductor manufacturing, electronics assembly, pharmaceuticals, food processing, consumer goods manufacturing, and many others.
In some cases, pattern detection methods can model patterns using one or more probes. A probe can refer to a position in an image at which the pattern detection methods examine a gradient vector of the image. Therefore, each probe can be associated with a position vector and an orientation vector. Since probes can effectively indicate the position and orientation of a pattern in an image, a machine vision system can use the probes to align the position and orientation of patterns.